Load Balancing Caching Data Partitioning

load balancing

features:

  1. spread traffic acorss cluster
  2. keep track of status of all resources while distributing request
  3. prevent single point failure

Note: Layer 7 Application traffic: has info from app layer -> by request type to the server

application on 3 places:

user <LB> web server <LB> internal platform/app server/cache server <LB> DB

caching

short term memory locality of reference principle: HW, OS, Web browsers, web app

Application serer cache

for multiple nodes, if the same request is forwarded to different node, the local node cache will not be reused, which increases the cache missing, how to improve this?

  1. global cache
  2. distributed cache

CDB Content Delivery Network

large amounts of static media if the system is not large enough to have itā€™s own CDN, we can ease a future transition by serving the static media off a separate subdomain using lightweight HTTP server like Nginx, and cut-over the DNS from your servers to CDN later. step1

public v.s. private IP: A public IP address identifies you to the wider internet so that all the information youā€™re searching for can find you. A private IP address is used within a private network to connect securely to other devices within that same network. Each device within the same network has a unique private IP address

why Nginx? route user request to different ports with load balancing can set cache

Apache HTTP server v.s. Nginx c10K? Nginx: Event driver, more used site use the nginx Apache Https: process non blocking

Cache Invalidation

wirte-through cache:

write -> cache -> DB: no data loss: only after two-level write, return success higher latency

write-around cache:

write -> -> DB, bypass the cache suitable: highly write, seldom re-read re-read: -> cache miss

write-back cache

write -> cache ->(certain condition/interval) -> DB -> high throughput && low latency

Cache Eviction policy:

FIFO/LIFO/LRU/MRU/LFU/RR

Data Pertitioning

horizontally scale: big DB/table -> mutiple machines:

Partitioning method:

horizontally v.s vertically partition

  • Horizontal Partitioning(data sharding): imbalanced server load if range not pick carefully

  • Vertically Partitioning: a feature a server if additional growth: for a certain feature, still need to further horizontal partition Straightforward to implement. Low impact on the application

  • Dicrectory-based A lookup service for partitioning schema and abstracts it away from the db access code allow DB server to change partition schema without impacting app cons: single point failure.

Partitioning Criteria

a. partition number = hash(keysā€¦) -> partition server cons: server number is fixed; consistent hashing can solve this problem. b. list partitioning: a bunch of key will go to a certain partition server c. round-robin partitioning: uniform data distribution d. composite partitioning: e.g. list + hash -> consistent hashing

data denormalization

: https://www.youtube.com/watch?v=T3cx09FINuU flattening data, redundant data

Common Problem

Constraints: The fact that operations across multiple tables or multiple rows in the same table will no longer run on the same server a. Join and Denormalization: cons: perform joins that span database partitions -> not efficient denormalize database -> joins on a single table cons of denormalization -> data inconsistency b. referential integrity foreign key: needs application code to enforce, if the RDBMS doesnā€™t support foreign keys/referential integrity

c

  1. date imbalanced
  2. request imbalanced/.